Automatic method for estimating the state of charge of a cell of a battery

ABSTRACT

A method for estimating state-of-charge of a battery cell includes suppressing full execution of a capacitance-estimation algorithm for as long as a parameter does not exceed a certain threshold. The parameter is either a measured voltage value across the cell&#39;s terminals, the cell&#39;s estimated state-of-charge, or an amount of charge passing in or out of the cell during some interval. A battery-management system triggers full execution when the parameter falls below the threshold, or, if the parameter is the amount of charge passing, when it rises above the threshold.

RELATED APPLICATIONS

This application is the national stage of PCT/FR2015/053242, which wasfiled on Nov. 26, 2015, which claims the benefit of the Nov. 28, 2014priority date of French application FR1461617, the contents of which areherein incorporated by reference.

FIELD OF INVENTION

The invention relates to battery management, and in particular, toestimating a battery's state-of-charge.

BACKGROUND

As a battery becomes older, it becomes less able to hold charge. It istherefore useful to be able to determine the state-of-charge of abattery.

A known method for estimating the state of charge of a cell of a batterycan be found in L. Plett, et al.: “Extended Kalmanfiltering for batterymanagement systems of LiPB-based HEV battery packs”, Journal of PowerSources, 2004, page 252-292. Below, this article will be designated bythe abbreviation “Plett 2004.”

SUMMARY

The invention uses a state model to estimate a cell's state-of-charge. Astate model has certain parameters that are used to estimatestate-of-charge. These parameters, among which is the cell'scapacitance, tend to change more slowly than the cell's state-of-charge.As a result, it is useful to estimate such parameters at a lowerfrequency than that at which the cell's state-of-charge is estimated.This reduces the computational burden of estimation withoutsignificantly degrading the estimate itself.

As the frequency with which capacitance is estimated decreases,inevitably, the overall estimate of the cell's state of charge willdeteriorate. Thus, it is important not to reduce this frequency toomuch.

The invention concerns controlling the frequency at which thiscapacitance is estimated. The method described herein automaticallytriggers estimation of capacitance C_(n,k3) in response to a thresholdbeing crossed. This results in an adaptive estimation frequency thatcorresponds to the cell's actual usage.

Moreover, the choice of the predetermined threshold makes it possible toavoid unnecessarily estimating the capacitance. In doing so, it limitsthe computational burden associated with estimation while also avoidingthe degradation of the estimate.

In addition, triggering capacitance-estimation so that it occurs onlywhen a state-of-charge threshold or a voltage threshold or anamount-of-charge-passed threshold has been crossed makes it possible tomake only one estimate per cycle of charging and discharging of thecell. This is largely sufficient for estimating the capacitance withenough precision.

In some practices, estimating the capacitance C_(n,k3) at each instant kby only calculating a prediction of this capacitance, without correctingthis prediction makes it possible to benefit, at each instant k, from anup-to-date estimate of the capacitance C_(n,k3). Thus, the precision ofthe estimation of the state of charge of the cell is increased. Inaddition, this does not substantially increase the computational burdenon the battery-management system because the step of calculating theprediction consumes less in the way of computational resources thanactually correcting the prediction.

Other practices offer the advantage of triggering the estimation of theinternal resistance RO_(k2) only when the measured current i_(k) exceedsa preset threshold. This improves the estimate for internal resistancebecause the ammeter's estimates are more accurate when the current ishigher. These practices thus manage to improve the estimate whilesimultaneously reducing the computational burden of obtaining thatestimate.

Yet other practices feature estimating the internal resistance at eachinstant by only calculating a prediction of this internal resistancewithout correcting the prediction. These practices make it possible tobenefit, at each instant k, from an up-to-date estimate of the internalresistance. As a result, the estimate of the cell's state-of-chargeimproves without substantially increasing the computational burden ofobtaining that estimate. This is because actually calculating aprediction consumes less in the way of computational resources thancorrecting the prediction.

Other practices use voltage values that have been measured between twosuccessive estimates of the internal resistance to estimate the internalresistance. This improves the estimate of the resistance.

In another aspect, the invention features a tangible and non-transitorycomputer-readable medium having encoded thereon instructions forcarrying out the foregoing methods.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood upon perusal of the followingdescription, given solely as a nonlimiting example, and referring to thedrawings, in which:

FIG. 1 is a partial schematic illustration of an automobile outfittedwith a battery,

FIG. 2 is a schematic illustration of a circuit model of a cell of thebattery shown in FIG. 1;

FIG. 3 is a schematic illustration of an arrangement of estimators usedto estimate the cell's state-of-charge in the battery of the vehicle ofFIG. 1;

FIGS. 4 to 9 represent equations of different state models andobservation models used by the estimators of FIG. 3;

FIG. 10 is a flow chart of an estimation method used by the estimatorsof FIG. 3 to estimate the state-of-charge of the cell in FIG. 1;

FIG. 11 is a flow chart of a method for determining of thestate-of-charge of the battery of the vehicle of FIG. 1;

FIG. 12 is a flow chart of a method for scheduling the refresh times forestimates of the states-of-charge of different cells of a battery;

FIG. 13 is a flow chart illustrating different scheduled refresh timeswith the aid of the method of FIG. 12;

FIG. 14 is a schematic illustration of another arrangement of estimatorsused to estimate the cell's state-of-charge of the battery of thevehicle of FIG. 1;

FIGS. 15 and 16 represent, respectively, a state model and anobservation model used by the estimators of FIG. 14;

FIG. 17 is a flow chart of a method for estimating the cell'sstate-of-charge with the aid of the estimators of FIG. 14;

FIG. 18 is an illustration of another possible state model forpredicting the internal resistance and capacitance of a cell of abattery.

In these figures, the same references are used to denote the sameelements. In the remainder of this description, characteristics andfunctions well known to the person skilled in the art are not describedin detail.

DETAILED DESCRIPTION

FIG. 1 shows an electric vehicle 2 having an electric motor 4 thatcauses wheels 6 to rotate, thus propelling the vehicle 2 along a roadway8.

The battery 10 comprises first and second battery terminals 12, 14 forelectrical connection and several electric cells electrically connectedbetween the battery terminals 12, 14. The battery terminals 12, 14connect to the electric motor 4.

The battery cells are grouped into several stages. These stages connectin series between the first and second terminals 12, 14. Only two suchstages are shown for simplicity. The first stage comprises first andsecond cells 18, 19; the second stage comprises second and third cells20, 21. Each stage comprises several branches connected in parallel.Each branch of a stage comprises one electric cell or several electriccells in series. In the illustrated embodiment, the first stagecomprises two branches, with each branch having a single electric cell.The second stage is structurally identical to the first stage in theexample shown in FIG. 1. Except for minor manufacturing tolerances, allbattery cells are structurally identical. Accordingly, the estimationmethod is discussed only for the first cell 18. The ordinal adjective“first” is therefore omitted for brevity.

The cell's first and second electrical connection terminals 30, 32connect it electrically to the other cells and, ultimately, to thebattery's terminals 12, 14. The cell 18 is also fixed mechanically, withno degree of freedom, to the battery's other cells to form a pack ofcells.

The cell 18 receives electrical energy while it is being charged andloses electrical energy while it is being discharged, for example whenit is powering the motor 4. The complete discharging of a cell followedby its complete recharging constitutes what is known as acharging/discharging cycle, or simply the “cycle of a cell.” Althoughthe cell can be any type, the cells described in this embodiment arethose used in a lithium-ion polymer battery.

A cell 18 is characterized by its initial capacitance G_(n) ^(ini), itsinitial internal resistance RO^(ini), its maximum current I_(max), itsmaximum voltage U_(max), its minimum voltage U_(min), and a functionOCV(SOC_(k)). The initial capacitance C_(n) ^(ini) is the capacitance ofthe cell 18 when it is brand new. The capacitance of a cell governs themaximum quantity of electric energy that can be stored in that cell,which is usually expressed in ampere-hours. As the cell 18 sustainscharging and discharging cycles, it ages. This decreases itscapacitance. Throughout this disclosure, the capacitance of the n^(th)cell 18 at time k shall be denoted as C_(n,k).

A cell's initial internal resistance RO^(ini) is the value of theinternal resistance of the cell 18 when it is brand new, and before ithas had time to age. A cell's internal resistance is a physical quantitythat is found in most circuit models of an electrical cell. As a cellages, its internal resistance tends to increase. Throughout thisdisclosure, the internal resistance of the cell 18 at time k shall bedenoted as RO_(k).

The maximum current I_(max) is the maximum current that can be deliveredby the cell 18 without the cell becoming damaged.

The maximum voltage U_(max) is the maximum voltage that can be presentconstantly between the cell's first and second terminals 30, 32 withoutdamaging the cell. The minimum voltage U_(min) is the minimum voltagebetween the cell's first and second terminals 30, 32 when the cell 18 iscompletely discharged.

Throughout the discussion that follows, the values of I_(max), U_(max),U_(min) are regarded as constant physical quantities that do not varyover time.

OCV(SOC_(k)) is a predetermined function that returns the no-loadvoltage of the cell 18 as a function of its state-of-charge SOC_(k). Theno-load voltage is the voltage measurable between the cell's first andsecond terminals 30, 32 after the cell 18 has been electricallyinsulated from any electric load for two hours.

The cell's state-of-charge at time k is denoted SOC_(k). It is equal to100% when the quantity of electric energy stored in the cell 18 is equalto whatever its capacitance C_(n,k) permits. It is equal to 0% when thequantity of electric energy stored in the cell 18 is zero, that is, whenno electric energy can be extracted from the cell 18 to energize anelectric load.

The parameters C_(n) ^(ini), RO^(ini), I_(max), U_(max), U_(min) and thefunction OCV(SOC_(k)) are known parameters of the cell. They are eitherprovided by the cell's manufacturer or obtained by carrying outmeasurement son the cell

The battery 10 also includes, for each cell, a voltmeter 34 and anammeter 36. The voltmeter 34 measures the voltage between the cell'sfirst and second terminals 30, 32. The ammeter measures the cell'scharging or discharging current. To simplify FIG. 1, only one voltmeter34 and one ammeter 36 of the cell 18 are shown.

Unlike the different parameters of the cell 18 introduced above, thecell's state-of-charge SOC_(k) is not measurable. Thus, it needs to beestimated. The vehicle 2 thus also includes a battery-management system40 carries out this function.

The battery-management system 40 determines the battery'sstate-of-charge and also its state-of-health. To determine thestate-of-charge and this state-of-health, the battery-management system40 estimates the state-of-charge and the state-of-health of each cell ofthe battery 10. A cell's state-of-health at time k, referred to hereinas “SOHk,” represents how much that cell has aged. A suitable metric forSOH_(k) is the ratio C_(n,k)/C_(n) ^(ini). To calculate the cell'sstate-of-health, the battery-management system 40 must therefore alsoestimate its capacitance C_(n,k) at the present time k.

To perform these various estimates, the battery-management system 40connects to each voltmeter and each ammeter of the battery 10. Thispermits the battery-management system 40 to acquire measurements ofvoltage and current associated with each cell.

As shown in FIG. 1, the battery-management system 40 comprises a memory42 and a programmable electronic computer 44, that is able to executeinstructions recorded in the memory 42. For this purpose, the memory 42contains the necessary instructions for the execution of the methodsshown in FIGS. 10 to 12 and/or FIG. 17. The memory 42 also contains theinitial values of the different parameters needed to execute thesemethods.

FIG. 2 represents a circuit model 50 of the cell 18. This model is knownas a “first-order Thévenin model” or “lumped parameter model.” Itcomprises, connected in succession in series starting from the cell'ssecond terminal 32 and ending at the cell's first terminal 30: agenerator 52 of no-load voltage OCV(SOC_(k)), a parallel RC circuit 54,and an internal resistance 56 referred to hereinafter, at time k,“internal resistance RO_(k).”

The parallel RC circuit 54 comprises a capacitor C_(D) connected inparallel to a resistor of value R_(D). In the discussion that follows,these values are presumed to be known and constant. The voltage at timek across the terminals of the parallel RC circuit 54 is denoted asv_(D,k). The value at time k of the voltage across the cell's first andsecond terminals 30, 32 is denoted as y_(k) and the charging ordischarging current of the cell 18, at the same time, is denoted as ik.These will be referred to as “measured voltage” and “measured current.”

FIG. 3 represents an arrangement of first, second, and third estimators60, 66, 68 implemented in the battery-management system 40 to estimatethe cell's state-of-charge and its state-of-health. Each estimator 60,66, 68 is implemented as digital circuitry that has been configured tocarry out a particular estimation algorithm. This is convenientlycarried out by presenting a sequence of bit patterns stored in thememory 42 in sequence to the computer 44 to cause digital circuitrywithin the computer 44 to be reconfigured so that the computer 44effectively becomes a new machine that carries out the relevantestimation. Although a computer represents an effective choice, anapplication-specific integrated circuit can also be used.

The first estimator 60 estimates the state-of-charge SOC_(k) and thevoltage v_(D,k) based on the measured voltage y_(k) and the measuredcurrent ik. The first estimator 60 is implemented in the form of aKalman filter. It thus uses a first state model 62, shown in FIG. 4, anda first observation model 64, shown in FIG. 5.

In FIGS. 4 and 5, the equations that define these models are representedusing the previously defined notation. The notations RO_(k2) andC_(n,k3) represent, respectively, the capacitance and the internalresistance of the cell 18 respectively, at times k2 and k3. These timesk2 and k3 shall be defined afterwards. In the first state model 62,x_(k) denotes the state vector [SOC_(k), v_(D,k)]^(T) at time k. In thisdescription, the symbol “^(T)” denotes the vector transpose.

In the following, the temporal origin corresponds to the zero value oftime k. In these conditions, the present time k is equal to kT_(e),where T_(e) is the sampling period for the measurements of the battery'sammeters 36 and voltmeters 34. Thus, T_(e) is the period of timeseparating any two consecutive sampling times k and k−1 at which thebattery-management system 40 receives a voltage and current measurement.The period T_(e) is typically a constant between 0.1 seconds and 10seconds. Preferably, the period T_(e) is equal to 1±0.2 seconds or 1second.

In the first state model 62, w_(k) is a state noise vector. In theembodiment described herein, the noise w_(k) is centered Gaussian whitenoise. This noise represents the uncertainty in the model used. Thecovariance matrix, at time k, of the noise w_(k) is denoted as Q_(k). Itis defined by Q_(k)=E(w_(k)·w_(k) ^(T)), where E( . . . ) is themathematical expectation function. The first state model 62 is likewisewritten in the form X_(k+1=F) _(k)x_(k)+B_(k)ik+w_(k), where F_(k) isthe state transition matrix at time k and B_(k) is the control vector attime k. The first state model 62 thus allows prediction of thestate-of-charge SOC_(k+1) at time k+1 from the preceding state-of-chargeSOC_(k).

The first observation model 64 allows prediction of the measured voltagey_(k) at time k from the state-of-charge SOC_(k), the voltage v_(D,k),and the measured current ik. In this model, vk is centered Gaussianwhite measurement noise. The covariance matrix of the noise vk at time kis denoted R_(k) in the following. In the particular case describedhere, matrix R_(k) is a matrix of a single column and a single rowdefined by the relation R_(k)=E(vk·vk^(T)). The noise vk is independentof the noise w_(k) and of the initial state vector x₀.

The first observation model 64 is nonlinear since the functionOCV(SOC_(k)) is generally nonlinear. Because of this, the firstestimator 60 implements the extended version of the Kalman filter. Theextended version results in a linear observation model of the formy_(k)=H_(k)x_(k)+RO_(k2)·ik+vk. This is arrived at by linearizing thefirst observation model 64 in the neighborhood of the vector x_(k).Linearizing typically includes developing the first observation model 64into a Taylor's series in the neighborhood of the vector x_(k) anddisregarding contributions of the derivatives starting with the secondorder. The matrix H_(k) is thus equal to the first derivative of thefunction OCV in the neighborhood of the state-of-charge SOC_(k). Thislinearization of the first observation model 64 is typically carried outfor each new value of the state-of-charge SOC_(k).

The first estimator 60 needs to know the cell's capacitance C_(n,k3) andthe internal resistance RO_(k2) in order to be able to estimate thestate-of-charge SOC_(k+1). These both vary as the cell 18 ages. To takethis aging into account, the second estimator 66 estimates the internalresistance RO_(k2) from the measured value y_(k2), from the measuredcurrent ik₂, and from the state-of-charge SOC_(k2). The third estimator68 estimates the capacitance C_(n,k3) from the current ik₃ and thestate-of-charge SOCk3.

A cell's internal resistance and capacitance vary more slowly than doesits state-of-charge. Thus, in order to limit the computational burden ofestimating a cell's state-of-charge of the cell without significantlydegrading the resulting estimate's accuracy, the battery-managementsystem executes the second and third estimators 66, 68 less frequentlythan it does the first estimator 60. In what follows, the executiontimes of the second and third estimators 66 and 68 are denotedrespectively as k2 and k3 in order to distinguish them from the times k.The set of times k2 and the set of times k3 are subsets of the set oftimes k. Thus, between two successive times k2 and k2−1 and between twosuccessive times k3 and k3−1 there elapse several periods T_(e) andseveral times k.

The second and third estimators 66, 68 are also each implemented in theform of a Kalman filter. The second estimator 66 uses a second statemodel 70, which is shown in FIG. 6, and a second observation model 72,which is shown in FIG. 7. In these models, the noises w_(2,k2) andv_(2,k2) are centered Gaussian white noises. The covariances of thenoises w_(2,k2) and v_(2,k2) are denoted respectively as Q_(2,k2) andR_(2,k2). The second observation model 72 permits prediction of thevalue of a directly measurable physical quantity u_(k2), which is thesum of the last N measured values y_(k). It is defined by the followingrelation:

$u_{k\; 2} = {\sum\limits_{m = {k - N}}^{k}\; y_{k}}$where N is a whole number greater than one that is counted as will bedescribed below. In the above relation and in the second observationmodel 72, the time k is equal to the time k2.

The second observation model 72 considers not only the state-of-chargeSOC_(k), the voltage v_(D,k), and the current ik measured at time k=k2,but also the N previous estimates of the first estimator 60 and the Nprevious measured currents between the times k2 and k2−1. Consideringthe intermediate measurements and estimates between the times k2 andk2−1 improves the estimate of the internal resistance RO_(k2).

The third estimator 68 uses a third state model 74, shown in FIG. 8, anda third observation model 76, which is shown in FIG. 9. In the thirdstate model 74 and the third observation model 76, the noises w_(3,k3)and v_(3,k3) represent centered Gaussian white noise. The covariances ofthe noises w_(3,k3) and v_(3,k3) are denoted respectively as Q_(3,k3)and R3, k3. The third observation model 76 is a linear model. This meansthat a simple Kalman filter can be used for the third estimator 68instead of an extended Kalman filter.

The observation model 76 permits estimation of a directly measurablephysical quantity zk3. The physical quantity z_(k3) is the sum of thelast N measured currents ik, which is defined by the summation:

In the above summation and in the third observation model 76, the time kis equal to the time k3. This physical quantity z_(k3) takes intoaccount not only the current ik−1 measured at time k−1 preceding time k3but also the previous N currents measured between the times k3 and k3−1.In the above summation, N is a whole number greater than one, which iscounted as shall be described further below. It is not necessarily equalto the N introduced in the second observation model 72. Taking intoaccount intermediate measurements and estimates between the times k3 andk3−1 improves the estimate for the capacitance C_(n,k3).

FIG. 10 shows the overall procedure carried out by the first, second,and third estimators 60, 66, 68 as they cooperate to estimate a cell'sstate-of-charge.

The method starts with a covariance-adjustment phase 100 that includesadjusting the different covariance matrices that are needed to executethe first, second, and third estimators 60, 66, 68. The covarianceadjustment phase 100 includes three steps, one corresponding to each ofthe three estimators 60, 66, 68.

In a first covariance-adjustment step 102, the covariance matrices Q_(k)and R_(k) of the first estimator 60 are automatically adjusted with theaid of the following relations: Q_(k)=[N₀G_(0,k)(N₀)]⁻¹ and R_(k)=I,where N₀ is a predetermined whole number that is greater than 1, I isthe identity matrix, and G_(0,k)(N₀) is defined by:

${G_{o,k}\left( N_{o} \right)} = {\sum\limits_{i = 0}^{N_{o} - 1}\;{\left( F_{k}^{T} \right)^{i}H_{k}^{T}{H_{k}\left( F_{k} \right)}^{i}}}$

The whole number N₀ is generally chosen during the design of thebattery-management system 40 and then is set once and for all.Generally, N₀ is less than 100. In some embodiments, N₀ is between 5 and15. In other embodiments, N₀ is chosen equal to 10.

The use of the preceding relations considerably simplifies theadjustment of the matrices Q₀ and R₀ as well as the adjustment of thematrices Q_(k) and R_(k). In fact, the only parameter to be chosen isthe value of the integer N₀.

During a second covariance-adjustment step 104, the covariances Q_(2,0)and R_(2,0) are also adjusted. In some embodiments, Q_(2,0) is chosen tobe equal to [(β·RO^(ini))/(3·N^(C) _(eol)·Ns)]², where β is a constantchosen to be greater than or equal to 0.3 or 0.5 and preferably greaterthan 0.8 and generally less than three, N^(c) _(eol) is the predictednumber of charging and discharging cycles of the cell 18 before itreaches its end of life, and N_(S) is the number of times that theinternal resistance is estimated per charging and discharging cycle ofthe cell 18.

The constant β, expressed as a percentage divided by 100, represents thedifference between the value of the initial internal resistance RO^(ini)and its end of life value. Typically, β is set by the user or measuredexperimentally. N^(c) _(eol) is a number of cycles that can be measuredexperimentally or obtained from data provided by the cell'smanufacturer. N_(S) is set by the estimation method for estimating thestate-of-charge as implemented by the computer 44. In this embodimentthe internal resistance is estimated once per cycle. Consequently,N_(S)=1.

As an illustration, the covariance R_(2,0) is chosen equal to(2ε_(m)U_(max)/300)², where ε_(m) is the maximum error of the voltmeter34 expressed as a percentage.

Afterwards, the covariances Q_(2,k2) and R_(2,k2) are considered to beconstant and equal respectively to Q_(2,0) and R_(2,0).

In some embodiments, the covariance Q_(3,0) is set to be equal to[γ·C_(n) ^(ini)(3·N^(C) _(eol)·N_(S))]², where γ, expressed as apercentage divided by 100, represents the difference between the initialcapacitance C_(n) ^(ini) and the capacitance of the cell 18 at the endof life. The value of γ is a constant chosen to be between 0.05 and 0.8,preferably between 0.05 and 0.3. In the embodiment described herein,γ=0.2.

In some embodiments, the covariance R_(3,0) is chosen to be equal to[2·ε_(im)·I_(max)/300]², where ε_(im) is the maximum error of theammeter 36 expressed in percentage.

Afterwards, the covariances Q_(3,k3) and R_(3,k3) are considered to beconstant and equal respectively to Q_(3,0) and R_(3,0).

Once the covariance matrices have been adjusted, the estimation of thestate-of-charge of the cell 18 can commence.

During a measurement phase 110, at each time k the voltmeter 34 and theammeter 36 measure, respectively, the measured voltage y_(K) and themeasured current ik. The battery-management system 40 then immediatelyacquires these measurements and records them in the memory 42. Themeasurement phase 110 is repeated at each time k.

In parallel, the first estimator 60 executes an SOC-estimation phase 114during which it estimates the cell's state-of-charge at time k.

The SOC-estimation phase 114 begins with an SOC-prediction step 116during which the first estimator 60 calculates a state-of-chargeprediction SÔC_(k/k−1) and a voltage prediction V_(D,k/k−1) to predictthe cell's state-of-charge of the cell 18 and the voltage V_(D) at theterminals of the parallel RC circuit 54 shown in FIG. 2 at time k.

As used herein, the index k/k−1 indicates that the prediction for time kconsiders only the measurements obtained between the times 0 and k−1.This is referred to as an “a priori” prediction. The index k/k indicatesthat the prediction considers all of the measurements done between thetimes 0 and k. This is referred to as an “a posteriori” prediction. Thepredictions SÔC_(k/k) ⁻¹ and V_(D,k/k) ⁻¹ are calculated with the helpif the first state model 62 from the measured current ik⁻¹ and thecapacitance C_(n,k3). In the first state model 62, the state transitionmatrix F_(k) ⁻¹ is constant regardless of k and thus does not need to bereevaluated at each time k.

During a first covariance-predicting step 117, the first estimator 60likewise calculates the prediction P_(k/k−1) of a covariance matrix forthe error in estimating the state vector x_(k). In the embodimentdescribed herein, the prediction is given byP_(k/k−1)=F_(k−1)P_(k−1/k−1)F_(k−1) ^(T)+Q_(k−1).

These various matrices F_(k−1), P_(k−1/k−1) and Q_(k−1) have alreadybeen defined previously.

Then, during a linearizing step 118, the first estimator 60 constructsthe matrix H_(k) by linearizing the first observation model 64 aroundthe predictions SÔC_(k/k−1) and V_(D,k/k−1).

During a second covariance-updating step 120, the covariance matricesQ_(k) and R_(k) are automatically updated. In the embodiment describedherein, the second covariance-updating step 120 is identical to thefirst covariance-adjustment step 102, but with the further considerationof the matrix H_(k) constructed during the linearizing step 118.

After having completed the second covariance-updating step 120, thefirst estimator 60 proceeds with an SOC-prediction correcting step 122.During the prediction-correction step 160, the first estimator 60corrects the predictions SÔC_(k/k−1) and V_(D,k/k−1) based on adifference between the measured voltage y_(k) and a predicted value ofthe voltage 9k, the predicted value having been computed by the firstobservation model 64. This results in a prediction-discrepancy.

The SOC-prediction correcting step 122 includes first and secondprediction-correction operations. The first prediction-correctionoperation includes calculating the prediction ŷ_(k). The secondprediction-correction operation 126 includes the predictions SÔC_(k/k−1)and V_(D,k/k−1) and the matrix P_(k/k−1) to obtain the correctedpredictions SÔC_(k/k−1), V_(D,k/k) and P_(k/k).

During the first SOC-prediction correcting operation 124, the predictionŷ_(k) is calculated using the first observation model 64 with thestate-of-charge being set equal to SÔC_(k/k−1) and the value of thevoltage V_(D,k) being set equal to V_(D,k/k−1). For convenience, it isuseful to refer to the difference between the measured voltage y_(k) andits predicted value ŷ_(k) as the discrepancy, E_(k).

There are many methods for correcting the a priori estimates ÔC_(k/k−1)and V_(D,k/k−1) based on the discrepancy E_(k). For example, during thesecond SOC-prediction correcting operation 126, the estimates arecorrected with the help of the Kalman gain K_(k). The Kalman gain K_(k)is given by K_(k)=P_(k/k−1)H^(T) _(k)(H_(k)P_(k/k−1)H^(T) _(k)+R_(k))⁻¹.The a priori predictions are then corrected with the help of thefollowing relation: x_(k/k)=x_(k/k−1)+K_(k)E_(k).

The matrix P_(k/k−1) is corrected with the help of the followingrelation: P_(k/k)=P_(k/k−1)−K_(k)H_(k)P_(k/k−1).

The SOC-estimation phase 114 repeats at each time k when a new estimateof the state-of-charge of the cell 18 needs to be done. During each newiteration, the state vector x_(k−1) is initialized with the valuesobtained during the preceding iteration of the SOC-estimation phase 114for that cell 18.

In parallel, during a first comparison-step 130, the computer 44compares each new measurement of the current ik to a predeterminedcurrent threshold SH_(i). As long as the measured current ik falls shortof this threshold SH_(i), the execution of the second estimator 66 isinhibited. Conversely, once the measured current ik surpasses thisthreshold SH_(i), the second estimator 66 is immediately executed. Inmany embodiments, the threshold SH_(i) is greater than I_(max)/2.Preferably, it is greater than 0.8·I_(max) or even 0.9·I_(max).

The second estimator 66 executes a resistance-estimation phase 140during which it estimates the internal resistance RO_(k2) at time k2,where the time k2 is equal to the time k at which the measured currentik crosses the threshold SH_(i). The resistance-estimation phase 140includes a resistance-prediction step 142, a secondcovariance-predicting step 144, and a prediction-correction step 148.

During the resistance-prediction step 142, the second estimator 66calculates the a priori prediction RÔ_(k/k−1) of the internal resistancefrom the second state model 70.

Next, during the second covariance-prediction step 144, the secondestimator 66 calculates the prediction P_(2,k2/k2−1) of the covariancematrix of the error of estimation for the internal resistance. Forexample, this prediction is calculated with the help of the relation:P_(2,k2/k2−1)=P_(2,k2−1/k2−1)+Q_(2,0). It is apparent that the secondobservation model 72 is a linear function of the state variable. Thus,it is not necessary to linearize it in the neighborhood of theprediction RÔ_(k2/k2−1) to obtain the matrix H_(2,k2). In this case, thematrix H_(2,k2) is equal to −N.

During a resistance-prediction correction step 148, the second estimator66 corrects the prediction RÔ_(k2/k2−1) as a function of the differencebetween the measured physical quantity u_(k2) and a correspondingpredicted physical quantity û_(k2). In this operation, N is apredetermined positive integer that, in some embodiments, is greaterthan ten, and in other embodiments, is greater than thirty. The secondestimator 66 acquires the measured physical quantity u_(k2) as themeasured voltages y_(k) are measured and acquired.

In particular, the resistance-prediction correction step 148 includes afirst resistance-prediction-correction operation 150 and a secondprediction-correction operation 152.

During the first resistance-prediction-correction operation 150, thecomputer 44 acquires the measured physical quantity u_(k2) andcalculates the predicted physical quantity û_(k2). The acquisition ofthe measured physical quantity u_(k2) is done by adding up the last Nmeasurements of the measured value y_(k). The predicted physicalquantity û_(k2) is calculated with the help of the second observationmodel 72. In this second observation model 72, the value RO_(k2) is setto be equal to the previously calculated value RO_(k2/k2−1).

Next, during the second prediction-correction operation 152, the secondestimator 66 corrects the prediction RÔ_(k2/k2−1) as a function of thediscrepancy E_(k2). The discrepancy E_(k2) is equal to the differencebetween the measured physical quantity u_(k2) and the predicted physicalquantity û_(k2).

In the embodiment described herein, the secondresistance-prediction-correction operation 152 uses the same method asthat implemented during the second SOC-prediction correcting operation126. Thus, the second resistance-prediction-correction operation 152 isnot described here in further detail. The new estimate RO_(k2/k2) isthen used during the following executions of the first estimator 60 inplace of the previous estimate RO_(k,2) ⁻¹ _(/k,2) ⁻¹ .

Triggering execution of the second estimator 66 only when the measuredcurrent i_(k) is elevated improves the estimate of the internalresistance and does so with reduced computational load. In fact, asynergistic relationship arises because the measurement precision of theammeter increases as the current i_(k) increases.

Also in parallel with the measurement phase 110 and the SOC-estimationphase 114, the method involves a second comparison step 160 duringwhich, at each time k, the state-of-charge estimate SOC_(k) is comparedto a predetermined upper threshold SH_(soc). If the state-of-chargeestimate SOC_(k) falls below this threshold SH_(soc), the methodcontinues immediately with a first counting step 162 and a thirdcomparison step 164. Otherwise, the second comparison step 160 isrepeated at the next time k. In the embodiment described herein, thethreshold SH_(soc) lies between 90% and 100%.

During the first counting step 162, the computer 44 begins initializes acounter to be zero. It then increments the counter by 1 at each newmeasurement of the current ik since the start of the first counting step162. Moreover, at each time k, the measured current ik and thestate-of-charge estimate SOC_(k) are recorded and associated with thetime k in a database.

In parallel with the first counting step 162, during a third comparisonstep 164, the computer 44 compares each new state-of-charge estimateSOC_(k) with a predetermined threshold SL_(soc). As long as thestate-of-charge estimate SOC_(k) remains higher than the predeterminedthreshold SL_(soc), the first counting step 162 repeats at the followingtime k. Otherwise, as soon as the state-of-charge estimate SOC_(k) fallsbelow the predetermined threshold SL_(soc), the computer 44 immediatelytriggers execution of the third estimator 68 and stops incrementing thecounter. Thus, as long as the state-of-charge estimate SOC_(k) does notsurpass the predetermined threshold SL_(soc), execution of the thirdestimator 68 is inhibited. In the embodiment described herein, thethreshold SL_(soc) lies between 0% and 10%.

In those cases in which it executes, the third estimator 68 executes acapacitance-estimation phase 166 during which it estimates thecapacitance C_(n,k3) at time k3. Thus, the time k3 is equal to time k atwhich the execution of the third estimator 68 was finally triggered.

As was the case for the resistance-estimation phase 140, given that thethird estimator 68 is not executed at each time k. The time k3−1 doesnot correspond to the time k−1. On the contrary, the times k3 and k3−1are separated by an interval of time greater than or equal to NT_(e)where N is the number counted during the first counting step 162.

The parameters of the Kalman filter of the third estimator 68 areinitialized with the previous values of these parameters obtained at theend of the previous iteration at time k3−1 of the capacitance-estimationphase 166.

The capacitance-estimation phase 166 includes a capacitance-predictionstep 170, a third covariance-matrix predicting step 172, and acapacitance-prediction correction step 174.

The capacitance-prediction step 170 includes calculating the predictionC_(n,k3/k3−1) with the help of the third state model 74.

The third covariance-matrix predicting step 172 includes calculating aprediction P_(3,k3/k3−1) of the covariance matrix for the estimationerror associated with estimating the capacitance.

During the third covariance-matrix predicting step 172 and thecapacitance-prediction correction step 174, the matrix of observabilityH_(3,k3) is equal to [(SOC_(k)−SOC_(k−N))]·3600/(NT_(e)), where N is thenumber of times k that have elapsed between the time when the estimatedstate-of-charge dropped below the threshold SH_(soc) and the time whenthe estimated state-of-charge has dropped below the threshold SL_(soc).The value N of is equal to the value counted during the first countingstep 162.

The capacitance-prediction correction step 174 includes correcting thepredictions C_(n,k3/k3−1) and P_(3,k3/k3−1). This involves first andsecond capacitance-prediction correction operations 176, 178.

The first capacitance-prediction correction operation 176 includesacquiring the measured physical quantity z_(k3) and calculating theprediction {circumflex over (z)}_(k3) of the quantity zk3. Theacquisition of the quantity z_(k3) includes calculating the sum of thelast N currents measured between the times k−1 and k−N. The prediction{circumflex over (z)}_(k3) is obtained from the observation model 76.

During the second capacitance-prediction correction operation 178, thethird estimator 68 corrects the prediction C_(n,k3/k3 1) as a functionof the difference between the measured quantity z_(k3) and the predictedquantity {circumflex over (z)}_(k3) in order to obtain the a posterioriestimate of the capacitance C_(n,k3/k3). This correction is done forexample as described during second SOC-prediction correcting operation126.

Next, the capacitance C_(n,k3/k3) is sent to the first estimator 60,which proceeds to use it to estimate the state-of-charge of the cell 18at the times that follow.

Triggering execution of the third estimator 68 only after the cell 18has already been, for the most part, discharged promotes improvedestimation while also reducing the computational load associated withcarrying out the estimation.

At the end of capacitance-estimation phase 166, during anSOH-calculating step 180, the computer calculates the state-of-healthSOH_(k3) at time k3 based on a capacitance ratio: SOHk3=C_(n,k3)/C_(n)^(ini).

FIG. 11 represents a method of determining the battery'sstate-of-charge. At time k, the battery's state-of-is determined fromthe state-of-charge of each of its cells. For example, this is done inthe following way. This includes a summation step 190, in which thecomputer 44 determines the state-of-charge of each stage of the batteryby adding up the states-of-charge of each cell of that stage. This isfollows by an SOC-assignment step 192 in which the battery'sstate-of-charge is set to that of the state having the smalleststate-of-charge as determined during the summation step 190.

As illustrated by the method of FIG. 11, the determination of thebattery's state-of-charge at each time k requires only an estimate ofthe state-of-charge for each of its cells at time k. A first solutionthus includes executing in parallel, for each of the cells, theestimation method of FIG. 10 by executing the SOC-estimation phase 114at each time k. However, this requires considerable computational load.To limit the required computational load without significantly degradingthe estimate, it is possible to schedule the execution of thestate-of-charge estimates as described in connection with the methodshown FIG. 12.

The scheduling method of FIG. 12 is described in the simplified case ofusing only three priority levels: an elevated priority level, a mediumpriority level, and a low priority level. These priority levels governhow often a cell's state-of-charge will be estimated. By definition, acell whose state-of-charge gives it an elevated priority level must haveits state-of-charge re-estimated, or refreshed, at each time k and thusat a fundamental frequency f_(e).

A cell whose state-of-charge gives it only a medium priority level onlyhas to have its state-of-charge refreshed at frequency of a fraction ofthis fundamental frequency, for example, at a frequency f_(e)/3.

A cell whose state-of-charge gives it only a low priority level only hasto have its state-of-charge refreshed at frequency of that is an evensmaller fraction of this fundamental frequency, for example, at afrequency f_(e)/10.

In the illustrated example, there is a quota in place for elevated andmedium priority cells. States-of-charge will thus only be refreshed forlimited numbers of cells whose states of charge place them at either thehigh or medium priority levels.

To schedule the times at which the estimates of the state-of-charge ofeach of the cells need to be refreshed, the computer begins byassigning, during a priority-assignment step 198, a priority level toeach cell.

The priority-assignment step 198 starts with a first prioritizingoperation 200 during which the battery-management system 40 acquires themeasured voltage y_(k) across the terminals of each cell.

Next, during a second prioritizing operation 202, if the measured valuey_(k) is above an upper threshold SH_(y) or, on the other hand, below alower threshold SL_(y), and if the quota for elevated priority has notyet been met, the computer 44 then assigns to this cell the elevatedpriority level.

The upper threshold SH_(y) is greater than or equal to 0.9·U_(max) and,preferably, greater than 0.95·U_(max). The lower threshold SL_(y),isgreater than or equal to U_(min) and less than 1.1·U_(min), or1.05·U_(min). It is important to refresh frequently the estimate of thestate-of-charge of the cells whose voltage is close to U_(max) or on theother hand close to U_(min). In fact, an error in the estimate of thecell's state-of-charge in such a situation may lead to a degradation ofthe electrical and mechanical properties of that cell.

Next, for the other cells, during a third prioritizing operation 204 thecomputer 44 calculates the difference in voltage between the presentmeasured value y_(k) and a previous value y_(k−x), where X is apredetermined whole number greater than or equal to one and generallyless than 5 or 10. Here, X=1.

During a fourth prioritizing operation 205, the computer 44 identifiestwin cells. Cells are considered to be “twins” if, at the same time k,they have the same voltage difference and the same measured value y_(k).

To identify such twin cells, the computer 44 compares the voltagedifference and the measured value y_(k) of one cell to the voltagedifferences and the measured values y_(k) of the other cells at the sametime. For those cells that are twins to a particular cell, theparticular cell's identifier, as well as those of its twins, are groupedinto a set. This set is then recorded in the memory 42.

In the embodiment described herein, the above comparison is carried outfor each of the cells of the battery 10 whose identifier has not alreadybeen incorporated into one of the sets of twin cells so recorded. Once agroup of cells has been identified as a set of twin cells, a prioritylevel is assigned to only cell in that set. Thus, the fifth prioritizingoperation 206 and the following scheduling step 208 and SOC-estimationstep 210 are performed only for the cells not having any twin cell andfor a single cell of each set of twin cells.

During a fifth prioritizing operation 206, the computer 44 sorts thecells in decreasing order of the absolute value of the differencecalculated during the third prioritizing-operation 204. It then assigns,to the first cells of the resulting sorted list, any remaining placesavailable for cells with elevated priority levels. It then assigns theremaining cells to any remaining places available for cells with mediumpriority level to the next cells in the sorted list. Finally, anyremaining cells are assigned the lowest priority level.

Once a priority level has been assigned to each cell, the computer 44carries out a scheduling step 208 during which it schedules times forrefreshing the state of charge estimates for the various cells. Thisscheduling is carried out subject to the constraint that cells withhigher priority levels must have their estimates refreshed morefrequently than cells that have a lower priority level.

During the scheduling step 208, the computer 44 schedules the refreshtimes for estimating states-of-charge for the various cells as afunction of their respective priority levels. The scheduling step 208 iscarried out so as yield the correct estimation frequency for a cellbased on that cell's priority level.

One way to schedule refresh times is to have the computer 44 reserve thetimes at which estimates of elevated-priority level cells need to berefreshed. Next, the computer 44 reserves the times at which cells ofmedium priority level need to have their state-of-charge estimatesrefreshed. In doing so, the computer 44 avoids scheduling conflicts withrefresh times that have already been reserved. Finally, the computerrepeats this procedure for those cells assigned a low priority level.

To illustrate this, assume that an elevated priority level has beenassigned to the cell 18, a medium priority level has been assigned tothe cells 19 and 20, and a low priority level has been assigned to thecell 21. Furthermore, assume that during a period T_(e) the computerexecutes the SOC-estimation phase 114 of the method of FIG. 10 at mosttwice. The result obtained with these assumptions is represented in FIG.13.

In this figure, the times k to k+11 have been plotted along the x-axis.Above each of these times k, two boxes symbolize the fact that thecomputer 44 may, at each time k, execute the SOC-estimation phase 114 ofthe method of FIG. 10 twice. In each of these boxes, the number of thecell for which the SOC-estimation phase 114 is executed has beenindicated. When no number appears in this box, it means that the methodof FIG. 10 was not executed. This means that the computer can be usedfor other purposes, such as, for example execution of the second andthird estimators 66, 68.

Finally, during an SOC-estimation step 210, for each cell assigned apriority level the computer 44 executes the SOC-estimation phase 114 atthe scheduled time for this cell. Outside of these scheduled times, thecomputer 44 inhibits the full execution of the SOC-estimation phase 114for this cell. Likewise, the computer 44 also inhibits execution of theSOC-estimation phase 114 for the twin cells to which no priority levelhas been assigned.

In parallel, during a twin-suppression step 212, for each twin cell thathas no priority level assigned to it, the state-of-charge estimate forthat cell is set equal to the most recent state-of-charge estimatecarried out for a twin cell of this cell. Thus, the SOC-estimation phase114 is executed for only one of the twin cells in a set of twin cells.This makes it possible to reduce the computing power needed to determinethe state-of-charge of the battery without degrading the estimate.

Optionally, in parallel with step 210, at each time k the computer 44also executes an SOC-prediction step 214 during which it predicts thestate-of-charge for each of the cells that were not processed during theSOC-estimation step 210 at time k. The SOC-prediction step 214 consistsin executing only the SOC-prediction step 116, without executing theSOC-prediction correcting step 122, for all the cells for which at thesame time the full estimate of the SOC-estimation phase 114 was notexecuted. In fact, the SOC-prediction step 116 imposes a much smallercomputational load than the SOC-prediction correcting step 122 and thusit may be executed, for example, at each time k. Thus, when theSOC-prediction step 214 is carried out, one will have, at each time k, anew estimate of the state-of-charge for each of the cells of thebattery.

The prioritizing step 198 and the scheduling step 208 are repeated atregular intervals in order to update the priority level assigned to eachof these cells. This will update the refresh frequencies for estimatingthe states-of-charge of these cells. This method makes it possible tolimit the computing computational load associated with such estimateswithout significantly degrading the estimate.

In fact, the method of FIG. 12 exploits the fact that the cells whosevoltage differences are low are cells that are not discharged or chargedvery often, and therefore precisely those whose state-of-charge wouldnot be expected to change rapidly.

During the execution of the methods of FIGS. 10 and 11, each time thatthe state-of-charge SOC_(k) of a cell at a given time needs to be usedfor a calculation, the state-of-charge SOC_(k) is set equal to the laststate-of-charge estimated or predicted for this cell. In other words, itis considered that the state-of-charge remains constant between twosuccessive times when it is estimated or predicted.

It will likewise be noted that, whenever the computer 44 executes theSOC-estimation phase 114 for a cell, it retrieves the necessaryinformation for this execution from values obtained at the end of theprevious execution of this phase for the same cell. This is the case inparticular for the state variables, for example. It will be noted,however, that the time of previous execution is then not necessarily thetime k−1, but it can be the time k−3 or k−10 depending on the prioritylevel assigned to the cell.

Many other embodiments of the estimation method are possible.

For example, FIG. 14 represents another arrangement of estimators thatis identical to that shown in FIG. 3 except that the second and thirdestimators 66, 68 are replaced by a single combined estimator 230. Thecombined estimator 230 simultaneously estimates the capacitance and theinternal resistance of the cell 18. The combined estimator 230 isexecuted less frequently than the first estimator 60. The executiontimes of the combined estimator 230 are denoted as k4. The estimatedcapacitances and internal resistances are denoted C_(n,k4) and RO_(k4).The set of times k4 is a subset of the times k.

The combined estimator 230 estimates the capacitance C_(n,k4) and theinternal resistance RO_(k4) at the same time. It does so by implementinga Kalman filter that uses a fourth state model 232 and a fourthobservation model 234. These are shown in FIGS. 15 and 16 respectively.

FIG. 17 shows the operation of the combined estimator 230. This methodof FIG. 17 is identical to that described in connection with FIG. 10except that the first comparison step 130 and the capacitance-predictioncorrection step 174 have been replaced by a fourth comparison step 240,a second counting step 242, a fifth comparison step 244, and anestimation phase 246 for estimating capacitance and the internalresistance.

During the fourth comparison-step 240, the computer 44, at each time k,compares the measured value y_(k) with an upper threshold SH_(y2).Typically, the upper threshold SH_(y2) is greater than or equal to0.8·U_(max) or 0.9·U_(max). Only if the measured value y_(k) drops belowthis threshold SH_(y2) will the second counting step 242 and the fifthcomparison step 244 actually execute.

During the second counting step 242, the computer 44 initializes acounter at zero and then increments this counter by 1 at each new timek. Moreover, at each of these times k the measured current ik, themeasured voltage y_(k), the state-of-charge SOC_(k), and the estimatedvoltage V_(D,k) are recorded in a database and associated with this timek.

In parallel with the second counting step 242, at each time step k, thefifth comparison step 244 compares the new measured value y_(k) to alower voltage threshold SL_(y2) . This lower threshold SL_(y2) is lessthan or equal to 1.2·U_(min) or 1.1·U_(min) and greater than or equal toU_(min).

Once the measured value y_(k) drops below the lower threshold SL_(y2),the second counting step 242 stops incrementing the counter. Thecombined estimator 230 then begins execution. On the other hand, as longas the measured value y_(k) remains above the lower threshold SL_(y2),the combined estimator 230 never has to execute.

The combined estimator 230 executes an estimation phase 246. As before,the times k₄ and k₄−1 are separated by an interval of time greater thanor equal to NT_(e), where N is the value of the counter as incrementedduring the second counting step 242. The functioning of the combinedestimator 230 can be inferred from the functioning previously describedsecond and third estimators 66, 68. Thus, it will not be described herein further detail.

Other circuit models and thus other state models can be used to estimatethe cell's state-of-charge. Some practices omit the parallel RC circuit54 from the circuit model. On the other hand, other practices use a morecomplex circuit model that has several parallel RC circuits electricallyconnected in series with each other. The state model of the cell 18should thus be modified as a consequence in order to correspond to thenew circuit model of the cell. However, all that has been describedabove applies with no difficulty to such a modified state model.

Examples of modified state models are described in the patentapplication WO2006057468, the contents of which are herein incorporatedby reference.

The parameters R_(D) and C_(D) of the circuit model 50 can also beestimated instead of being considered to be predetermined constantparameters. For this purpose, these two parameters R_(D) and C_(D) areintroduced into the state vector x_(k), which then becomes [SOC_(k),V_(D,k), R_(D,k) and C_(D,k)]^(T). In that case, the state model ismodified to incorporate the following two equations R_(D,k+1 =R) _(D,k)and C_(D,k+1 =C) _(D, k).

In some embodiments, the cell's temperature is included in the statevector x_(k) In such embodiments, the temperature is estimatedconcurrently with the state-of-charge.

Embodiments also include those in which the cell has one or moresupplemental sensors to measure other physical quantities. This leads tomodified observation models, examples of which are given inWO2006057468.

Other possible circuit models to simulate the electrical cell are alsopresented in part 2 of Plett 2004, in chapter 3.3.

The automatic continual adjustment of the covariance matrices R_(k) andQ_(k) can be done in a different way. One alternative method iscovariance matching as described in Mehra, R. K: “On the identificationof variances and adaptive Kalman Filtering,” Automatic Control, IEEETransaction on, Volume 15, No. 2, pages 175-184, April 1970. This methodis applied after an initial setup of the matrices R₀ and Q₀, forexample, as described during the first covariance-adjustment step 102.

In another practice, the matrices Q₀, R₀, Q_(k) and R_(k) are notadjusted as described in connection with the first covariance-adjustmentstep 102 and the covariance-updating step 120. Instead, these matricesare adjusted by carrying out a conventional method. In a simplifiedcase, the matrices are constant. In some practices, the matrix R₀ is setup using data provided by the manufacturer of the sensors or based ontests performed on the sensors, and the matrix Q₀ is set up byconsecutive tests.

The SOC-prediction correcting step 122 or the secondcapacitance-prediction correction operation 178 for correction of theprediction can be done differently. For example, in one preferredmethod, the correction of the prediction of the state-of-charge and thevoltage V_(D,k) is done by minimizing a quadratic cost function Jcomposed a first term that is linked to the error of prediction of themeasured value, and a second term that is linked to the error ofestimation of the state vector. This method is described in detail inchapter 10.5.2 of Y. Bar-Shalom, et al.: “Estimation With Applicationsto Tracking and Navigation, Theory Algorithms and Software,” WileyInter-science, 2001.

In another practice, the first estimator 60 is not implemented as Kalmanfilter. Instead, the state-of-charge is estimated by simulating itsdevelopment over time using an infinite impulse response filter whosecoefficients are estimated by the recursive least squares method.

Other models of state can be used to estimate the cell's internalresistance and capacitance. For example, in some embodiments, a fifthstate model 250 replaces the fourth state model 232. The fifth statemodel 250, shown in FIG. 18, includes constants α, β and γ whose valuesare obtained from cell manufacturer data or measured experimentally.Typically, α is equal to 1±30% or 1±10%, β is also equal to 1±30% or1±10%, and γ is typically between 0.1 and 0.5. For example, γ is equalto 0.2±30% or 0.2±10%.

In the fifth state model 250, N^(c) _(k) is equal to the number ofcharging/discharging cycles of the cell performed prior to the time k.This number of cycles is measured for example by counting the number oftimes that the state-of-charge of the cell drops below the upperthreshold SH_(soc) then below the lower threshold SL_(soc). The noiseterm w^(a) _(d,k) represents centered Gaussian white noise. The value ofγ is the difference, expressed as a percentage divided by 100, betweenthe initial capacitance C_(n) ^(ini) of the cell and its end of lifecapacitance. This model accounts for the fact that, as a cell ages, itscell's internal resistance increases and its internal capacitancediminishes.

In a similar manner, the second state model 70 can be replaced by thestate model: RO_(k2+1)=(α+βN^(C) _(k2)/N^(C) _(EOL))RO_(k2)+w_(2,k2),where the different symbols of this model have already been definedpreviously.

The third state model 74 can be replaced by the state model:C_(n,k3+1)=(1-γN^(C) _(k3)/N^(C) _(EOL))C_(n,k3)+v_(3,k3) where thedifferent symbols of this model have already been defined previously.

Depending on the observation model used by the third estimator 68, thequantity z_(k3) can be calculated differently. In some practices, z_(k3)is equal to the sum of the last N currents measured between times k andk−N+1. In this case, when N is equal to 1, zk3=ik₃.

What was described above for the initialization of the covariancematrices Q_(k) and R_(k) can also be applied to the initialization ofthe covariance matrices of the third estimator 68 and the combinedestimator 230.

In alternative practices, the third estimator 68 is not implemented inthe form of a Kalman filter. In one such alternative practice, thecapacitance is estimated by simulating its development over time usingan infinite impulse response filter whose coefficients are estimated bythe recursive least squares method.

The methods of FIGS. 10 and 17 may be simplified by setting N to beequal to a predetermined constant. In this case, Nis not counted.Therefore, it is possible to omit the second comparison step 160, thefirst counting step 162, the fourth comparison step 240, and the secondcounting step 242. For example, N is chosen equal to one or, on theother hand, absolutely greater than one, or five, or ten.

In another practice, at each time k between the times k3 and k3−1 onlythe capacitance-prediction step 170 of calculation of a predictionC_(n,k) is executed, but capacitance-prediction correction step 174 ofcorrection of this prediction is not executed. Thus, one obtains a newprediction of the capacitance of the cell at each of these times k whileavoiding an excessive computational burden. In a similar fashion, ateach time k between the times k4 and k4−1, only the step of calculatingthe predictions of the capacitance and the internal resistance isexecuted without executing the step of correcting these predictions.Thus, in these practices, the capacitance of the cell is predicted ateach time k but this prediction is corrected only at the times k3 or k4.The algorithm for estimation of this capacitance is thus only partlyexecuted between times k3 and k3−1 or k4 and k4−1 and fully executedonly at time k3 or k4.

At each time k between times k3 and k3−1 or between times k4 and k4−1,the capacitance can be estimated by executing a first algorithm. Then attime k3 or k4 the capacitance is estimated by executing a secondalgorithm that is different from the first algorithm and that imposes agreater computational load. The first and second algorithms do notnecessarily correspond, as described above, to respectively thecapacitance-prediction step 170 and the estimation phases 166, 246 of aKalman filter. They may also be two completely different estimationalgorithms.

The capacitance-estimation phase 166 or the estimation phase 246 canalso be triggered in response the state-of-charge crossing a thresholdof-charge, as described in connection with FIG. 10. Alternatively, itcan be trigged in response to the voltage crossing a threshold asdescribed in connection with FIG. 17.

In some practices, the estimation phases 166, 246 are triggered inresponse to current crossing a threshold. To execute this procedure,starting with the time at which the voltage or the state-of-charge ofthe cell has dropped below a predetermined upper threshold, at each timek the computer 44 calculates the accumulated charge transferred QC_(k)with the help of the relationship QC_(k)=QC_(k−1)+ikT_(e). Once theaccumulated charge QC_(k) crosses an upper threshold SH_(Q), thecomputer 44 executes the estimation phases 166, 246. On the other hand,as long as the accumulated charge QC_(k) remains above the thresholdSH_(Q), the computer 44 continues to inhibit execution of the estimationphases 166, 246. In an alternative practice, the quantity QC_(k) mayalso be calculated on a sliding window containing the last N times k,where N is a predetermined constant.

Other practices omit triggering estimation of capacitance and/or theinternal resistance in response to crossing of a threshold. Among theseare practices that trigger these estimation steps at regular intervals.In cases where sufficient computational capacity is available, theregular interval is equal to T_(e).

Other practices of the method of FIG. 12 omit the fourth prioritizingoperation 205. In such cases, no twin cell is identified. Therefore, thetwin-suppression step 212 is also omitted.

Other practices either carry out the second prioritizing operation 202differently or omit it altogether. Among the former are practices inwhich only a single one of the upper and lower thresholds is used. Inother practices, the second prioritizing operation 202 is omittedaltogether.

Although two priority levels are necessary, any number beyond that isarbitrary.

Other methods for assigning a priority level to the cells are possible.For example, in some practices, the priority level of a cell may becalculated with the help of a formula linking its priority level to itsvoltage difference and its voltage. In these practices, the comparisonoperations are omitted.

The method described for associating the refresh times with the cells asa function of their priority levels is only one example. Any other knownmethod for ordering tasks as a function of the priority level of thesetasks can be adapted to ordering of the refresh times for estimatingstates-of-charge of the cells.

The scheduling of the refresh times for the estimation of thestate-of-charge of each of the cells as described in regard to FIG. 12may be omitted. For example, this will be the case if the computingpower needed for the estimation of the state-of-charge of each of thecells at each time k is available.

In an alternative practice, the computer 44 comprises severalprogrammable sub computers each of them able to execute in parallel themethod of estimation of FIG. 10 or 17 for respective cells.

Alternative practices calculate the cell's state-of-health of a cellusing a ratio of resistances, such as SOH_(K)=RO_(K)/RO^(ini).

Many kinds of battery 10 are possible, including a lead battery, a supercapacitance, or a fuel cell. In such case, the state model and/or theobservation model of the first estimator 60 are optionally adapted toaccommodate the peculiarities of the relevant battery technology.

What has been specified above also applies to a hybrid vehicle, that is,the vehicle whose driving of the powered wheels is provided at the sametime, or alternately, by an electric motor and a thermal internalcombustion engine. The vehicle 2 can be a truck, a motorbike or atricycle or, generally speaking, any vehicle capable of moving bydriving the power wheels with the aid of an electric motor energized bya battery. For example, it may be a hoist.

The battery 10 can be recharged with the aid of an electrical outletwhich allows it to be electrically connected to the electricity mains.The battery 10 can also be recharged by a thermal internal combustionengine.

Having described the invention, and a preferred embodiment thereof, whatwe claim as new, and secured by Letters Patent is:
 1. A methodcomprising causing a technical improvement to the technology ofestimating state-of-charge of a battery's cell, wherein causing saidtechnical improvement comprises using a battery-management system thatcomprises an electronic processor and a memory for automaticallyestimating state-of-charge of said battery's cell, wherein said batterypowers an electric motor that causes an electric vehicle to move along aroadway and to do so based at least in part on measurements obtained byan ammeter and a voltmeter that are connected to said battery-managementsystem, wherein said battery comprises cells that are grouped intostages that connect between first and second terminals thereof, whereineach of said stages comprises a plurality of branches that are connectedin parallel, each of which comprises either one cell or several cells inseries, wherein cell comprises connections that connect said cell toother cells and ultimately to said terminals of said battery, whereinsaid cell receives electrical energy while being charged and loseselectrical energy while being discharged, wherein a complete dischargingof a cell followed by a complete recharging constitutes a cycle of saidcell, wherein automatically estimating state-of-charge comprises, ateach of a plurality of first instants k, using a voltmeter, acquiring ameasured voltage y_(k) and, using an ammeter, acquiring a measuredcurrent ik, wherein said measured voltage is a voltage across saidcell's terminals, and wherein said measured current is a currentselected from the group consisting of a current that charges said celland a current that discharges said cell, estimating said cell'sstate-of-charge SOC_(k) based at least in part on said measured voltage,said measured current, and a capacitance C_(n,k3) of said cell, saidcapacitance being indicative of an amount of energy that can be storedby said cell at a second instant k3 from a plurality of second instants,said second instants occurring less frequently than said first instants,wherein said second instant is a second instant that is closer to saidfirst instant k than all other second instants in said plurality ofsecond instants, said capacitance having been estimated at said secondinstant k3, wherein estimating said cell's state-of-charge SOC_(k)comprises causing said electronic processor to inhibit full execution ofan algorithm for estimating capacitance C_(n,k3) of said cell based atleast in part on current i_(k3) measured at a second instant k3 when afirst condition is met, and causing said electronic processor to triggerfull execution of said algorithm when any one of a second condition, athird condition, and a fourth condition is met, wherein said firstcondition is that a value of a parameter has not crossed a first presetthreshold, said parameter being chosen from the group consisting of saidmeasured voltage y_(k), said estimate of said state-of-charge SOC_(k),and an amount Q_(k) of charge that has passed through said cell betweensaid instant k and a preceding instant, wherein said second condition isthat said parameter has dropped below said first preset threshold andthat said parameter is said measured voltage y_(k), wherein said thirdcondition is that said parameter has dropped below said first presetthreshold and that said parameter is said estimate of saidstate-of-charge SOC_(k), and wherein said fourth condition is that saidparameter has risen above said first preset hreshold and that saidparameter is said amount Q_(k) of charge that has passed through saidcell between said instant k and a preceding instant.
 2. The method ofclaim 1, wherein causing said electronic processor to trigger fullexecution of said algorithm comprises causing said processor tocalculate a prediction of said capacitance C_(n,k3) using a state modelthat relates a capacitance C_(n,k3) of said cell at a particular instantto a capacitance of said cell at a preceding instant k3−1 and to thencorrect said prediction based at least in part on current measured atsaid second instant, wherein said method further comprises, at eachfirst instant at which said processor inhibits full execution of analgorithm, causing said processor to calculate said prediction of saidcapacitance without subsequently correcting said prediction and causingsaid processor to use said uncorrected prediction as a basis forestimating state-of-charge of said cell at said first instant.
 3. Themethod of claim 1, wherein estimating said cell's state-of-chargeSOC_(k) further comprises estimating said state-of-charge based at leastin part on an internal resistance RO_(k2) of said cell that is estimatedat an instant k2 that is closest to said first instant k, wherein saidmethod further comprises causing said electronic processor to inhibitfull execution of an algorithm for estimating said internal resistanceRO_(k2) provided that said measured current i_(k) is lower than a presetcurrent threshold and causing said processor to trigger full executionof said algorithm in response to said current i_(k) having increasedabove said preset current threshold.
 4. The method of claim 3, whereincausing said processor to trigger full execution of said algorithm forestimating said internal resistance RO_(k2) comprises causing saidprocessor to calculate a prediction of said internal resistance RO_(k2)using a state model that relates internal resistance RO_(k2) of saidcell at a particular instant to internal resistance RO_(k2−1) of saidcell at a preceding instant k2−1, acquiring, at said instant k2, ameasurable physical quantity u_(k2), calculating a prediction of saidmeasured physical quantity, and correcting said prediction of saidinternal resistance RO_(k2) depending on a difference between saidacquired physical quantity u_(k2) and said calculated prediction û_(k2),wherein said measurable physical quantity is defined by:$u_{k\; 2} = {\sum\limits_{m = {k - N}}^{k}\; y_{k}}$ wherein k is aninstant closest to said second instant k2 and N is an integer higherthan or equal to zero, wherein said second physical quantity u_(k2) isequal to same measured voltage y_(k) when N is equal to zero, whereincalculation of said prediction û_(k2)of said measurable physicalquantity u_(k2) comprises evaluating an observation model given by:${\hat{u}}_{k\; 2} = {\sum\limits_{m = {k - N}}^{k}\;\left\lbrack {{{OCV}\left( {SOC}_{m} \right)} - V_{D,m} - {{RO}_{k\; 2}*i_{m}}} \right\rbrack}$wherein k is the instant closest said second instant k2, whereinOCV(SOC_(m)) is a function that expresses open-circuit voltage acrosssaid cell's terminals as a function of state-of-charge SOC_(m) of saidcell at an instant m, wherein V_(D,m) is a voltage across terminals of aparallel RC circuit, and wherein RO_(k2) is, in this observation model,the prediction of said internal resistance of said cell at said instantk2 and prior to said prediction being corrected.
 5. The method of claim4, wherein at each instant k at which full execution of said algorithmfor estimating said internal resistance RO_(k2)is inhibited, said methodincludes calculating said prediction of said internal resistanceRO_(k2), without correcting this prediction of said internal resistanceRO_(k2), and using this uncorrected prediction of said internalresistance RO_(k2) in said estimation of said state-of-charge SOC_(k) ofsaid cell.
 6. The method of claim 4, wherein N is greater than one. 7.An apparatus comprising a battery-management system for managing abattery equipped with at least one cell, wherein said battery-managementsystem comprises a processor configured to execute said method ofclaim
 1. 8. The apparatus of claim 7, further comprising a motor vehiclecomprising a drive wheel, an electric motor, a battery, a voltmeter, andan ammeter, wherein said electric motor is configured to rotate saidwheel to move said motor vehicle, wherein said battery including atleast one cell able to store electrical energy and, in alternation,release said electrical energy to power said electrical motor, said cellincluding two terminals by way of which said cell is electricallyconnected to said electrical motor, wherein said voltmeter iselectrically connected between said terminals of said cell in order tomeasure voltage across said terminals, wherein said ammeter is connectedin series with said electrical cell, in order to measure currentcharging or discharging said cell, and wherein said battery-managementsystem is electrically connected to said voltmeter and to said ammeter.9. A manufacture comprising a non-transitory and tangiblecomputer-readable medium having encoded thereon instructions for causingan electronic processor to execute said method recited in claim 1.